Predictability and Risk of Extreme Winter PM2.5 Concentration in Beijing
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s13351-023-3023-8.pdf
Reference46 articles.
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2. Chen, G. X., T. Iwasaki, H. L. Qin, et al., 2014: Evaluation of the warm-season diurnal variability over east Asia in recent reanalyses JRA-55, ERA-interim, NCEP CFSR, and NASA MERRA. J. Climate, 27, 5517–5537, doi: https://doi.org/10.1175/JCLI-D-14-00005.1.
3. Chuluunsaikhan, T., M. Heak, A. Nasridinov, et al., 2021: Comparative analysis of predictive models for fine particulate matter in Daejeon, South Korea. Atmosphere, 12, 1295, doi: https://doi.org/10.3390/atmos12101295.
4. Dash, U. K., S.-Y. Park, C. H. Song, et al., 2023: Performance comparisons of the three data assimilation methods for improved predictability of PM2.5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods. Environ. Pollut., 322, 121099, doi: https://doi.org/10.1016/j.envpol.2023.121099.
5. Ding, Y. H., and Y. J. Liu, 2014: Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity. Sci. China Earth Sci., 57, 36–46, doi: https://doi.org/10.1007/s11430-013-4792-1.
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